Mike Lewis

@ml_perception

NLP researcher at Facebook AI Research in Seattle

Vrijeme pridruživanja: rujan 2019.

Tweetovi

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  1. Prikvačeni tweet
    30. lis 2019.

    Excited to share our work on BART, a method for pre-training seq2seq models by de-noising text. BART outperforms previous work on a bunch of generation tasks (summarization/dialogue/QA), while getting similar performance to RoBERTa on SQuAD/GLUE

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  2. 10. pro 2019.

    At NeurIPS to present our work on agents that "think in language" by generating and then executing plans in the form of natural language instructions. Code and a large new dataset are online!

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  3. proslijedio/la je Tweet
    3. stu 2019.

    Generalization through Memorization: Nearest Neighbor Language Models Reduces ppl from 18.27 to 15.79 (sota) in Wikitext-103 using kNN and pretrained Wikitext LM without further training.

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  4. 3. stu 2019.

    Improve your language model by converting it into a deep nearest neighbour classifier! The amazing pushes SOTA on Wikitext-103 by nearly 3 points, without any additional training (and gets a few other surprising results too).

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  5. 30. lis 2019.
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  6. 30. lis 2019.

    Joint work with Naman Goyal Abdelrahman Mohamed,

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  7. 30. lis 2019.

    This does seem closely related to T5 from Google last week. I haven't read that in detail yet, but it seems like we use a slightly different pre-training objective, and better results for the same model size. We haven't tried training an 11B parameter model yet, though :-)

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  8. 30. lis 2019.

    I found the summarization performance surprisingly good - BART does seem to be able to combine information from across a whole document with background knowledge to produce highly abstractive summaries. Some typical examples beneath:

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  9. proslijedio/la je Tweet
    25. ruj 2019.

    Impressed by Ovid's 🦄 but want a deeper eval of GPT2 open-ended NLG? See our paper "Do Massively Pretrained Language Models Make Better Storytellers?" Work with

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